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Development","award":["N2123004"],"award-info":[{"award-number":["N2123004"]}]},{"name":"Administration of Central Funds Guiding the Local Science and Technology Development","award":["206Z1702G"],"award-info":[{"award-number":["206Z1702G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aircrafts have been an important object of study in the field of multi-scale image object detection due to their important strategic role. However, the multi-scale detection of aircrafts and their key parts from remote sensing images can be a challenge, as images often present complex backgrounds and obscured conditions. Most of today\u2019s multi-scale datasets consist of independent objects and lack mixed annotations of aircrafts and their key parts. In this paper, we contribute a multi-scale aircraft dataset (AP-DATA) consisting of 7000 aircraft images that were taken in complex environments and obscured conditions. Our dataset includes mixed annotations of aircrafts and their key parts. We also present a multi-scale information augmentation framework (MS-IAF) to recognize multi-scale aircrafts and their key parts accurately. First, we propose a new deep convolutional module ResNeSt-D as the backbone, which stacks scattered attention in a multi-path manner and makes the receptive field more suitable for the object. Then, based on the combination of Faster R-CNN with ResNeSt-D, we propose a multi-scale feature fusion module called BFPCAR. BFPCAR overcomes the attention imbalance problem of the non-adjacent layers of the FPN module by reducing the loss of information between different layers and including more semantic features during information fusion. Based on AP-DATA, a dataset with three types of features, the average precision (AP) of MS-IAF reached 0.884, i.e., 2.67% higher than that of the original Faster R-CNN. The APs of these two modules were improved by 2.32% and 1.39%, respectively. The robustness of our proposed model was validated using the open sourced RSOD remote sensing image dataset, and the best accuracy was achieved.<\/jats:p>","DOI":"10.3390\/rs14153696","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:15:26Z","timestamp":1659485726000},"page":"3696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MS-IAF: Multi-Scale Information Augmentation Framework for Aircraft Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuliang","family":"Zhao","sequence":"first","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5880-2565","authenticated-orcid":false,"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1794-8976","authenticated-orcid":false,"given":"Weishi","family":"Li","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"given":"Peng","family":"Shan","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"given":"Xiaoai","family":"Wang","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"given":"Lianjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"Shijiazhuang School, Army Engineering University of PLA, Shijiazhuang 050003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TIP.2021.3065239","article-title":"SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection","volume":"30","author":"Liu","year":"2021","journal-title":"IEEE Trans. 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